Combining the benefits from both geometric theory and deep learning, hybrid models are normally more accurate than end-to-end VO at this stage, as shown in Table 1.
It is notable that hybrid models even outperform the state-of-the-art conventional monocular VO or visual-inertial odometry (VIO) systems on common benchmarks, for example, D3VO[1]} defeats several popular conventional VO/VIO systems, such as DSO[2]}, ORB-SLAM[3]}, VINS-Mono[4]}. This demonstrates the rapid rate of progress in this area.
